Comparative Analysis of Machine Learning Regression Algorithms on Air Pollution Dataset
DOI:
https://doi.org/10.32628/CSEIT206427Keywords:
Machine Learning, Regression, Prediction, Air Quality Index, So2, PM2.5, Accuracy score, Support Vector Regression.Abstract
Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. Examining and protecting air quality has become one of the most essential activities for the government in many industrial and urban areas today. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. The main objective of this paper to build a model for predicting Air Quality Index(AQI) of the specific cities using various types of machine learning algorithms namely Multiple Linear Regression, K Nearest Neighbours(KNN), Support Vector Machine(SVM) and Decision Tree. And also evaluate and compare the performance of every algorithm based on their accuracy score and errors. Air Pollution dataset is publicly available on different government sites. The implementation phase dataset is divided as 80% for the training of different models and the rest of the dataset is used for testing the model.
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